Healthy biological systems exhibit complex patterns of variability that can be described by mathematical chaos. Heart rate variability (HRV) consists of changes in the time intervals between consecutive heartbeats called interbeat intervals (IBIs). A healthy heart is not a metronome. The oscillations of a healthy heart are complex and constantly changing, which allow the cardiovascular system to rapidly adjust to sudden physical and psychological challenges to homeostasis. This article briefly reviews current perspectives on the mechanisms that generate 24 h, short-term (~5 min), and ultra-short-term (<5 min) HRV, the importance of HRV, and its implications for health and performance. The authors provide an overview of widely-used HRV time-domain, frequency-domain, and non-linear metrics. Time-domain indices quantify the amount of HRV observed during monitoring periods that may range from ~2 min to 24 h. Frequency-domain values calculate the absolute or relative amount of signal energy within component bands. Non-linear measurements quantify the unpredictability and complexity of a series of IBIs. The authors survey published normative values for clinical, healthy, and optimal performance populations. They stress the importance of measurement context, including recording period length, subject age, and sex, on baseline HRV values. They caution that 24 h, short-term, and ultra-short-term normative values are not interchangeable. They encourage professionals to supplement published norms with findings from their own specialized populations. Finally, the authors provide an overview of HRV assessment strategies for clinical and optimal performance interventions.

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An Overview of Heart Rate Variability Metrics and Norms.

Sleep, sleep deprivation, autonomic nervous system and cardiovascular diseases.

Heart rate variability (HRV), the beat-to-beat variation in either heart rate or the duration of the R-R interval, has become a popular clinical and investigational tool (Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology, 1996; Billman, 2011). Indeed, the term “heart rate variability” yields nearly 18,000 “hits” when placed in the pubmed search engine. These temporal fluctuations in heart rate exhibit a marked synchrony with respiration (increasing during inspiration and decreasing during expiration—the so called respiratory sinus arrhythmia) and are widely believed to reflect changes in cardiac autonomic regulation (Billman, 2011). Although the exact contributions of the parasympathetic and the sympathetic divisions of the autonomic nervous system to this variability are controversial and remain the subject of active investigation and debate, a number of time and frequency domain techniques have been developed to provide insight into cardiac autonomic regulation in both health and disease (Billman, 2011). It is the purpose of this book to provide a comprehensive assessment of the strengths and limitations of HRV techniques. Particular emphasis will be placed on the application of HRV techniques in the clinic and on the interaction between prevailing heart rate and HRV. This book contains both state-of-the art review and original research articles that have been grouped into two main sections: Methodological Considerations and Clinical Application. A brief summary of the chapters contained in each section follows below.

/pdf/an-introduction-to-heart-rate-variability-methodological-3ym9ve9zqe.pdf

An introduction to heart rate variability: methodological considerations and clinical applications.

Driver drowsiness is a major cause of fatal accidents, injury, and property damage, and has become an area of substantial research attention in recent years. The present study proposes a method to detect drowsiness in drivers which integrates features of electrocardiography (ECG) and electroencephalography (EEG) to improve detection performance. The study measures differences between the alert and drowsy states from physiological data collected from 22 healthy subjects in a driving simulator-based study. A monotonous driving environment is used to induce drowsiness in the participants. Various time and frequency domain feature were extracted from EEG including time domain statistical descriptors, complexity measures and power spectral measures. Features extracted from the ECG signal included heart rate (HR) and heart rate variability (HRV), including low frequency (LF), high frequency (HF) and LF/HF ratio. Furthermore, subjective sleepiness scale is also assessed to study its relationship with drowsiness. We used paired t-tests to select only statistically significant features (p < 0.05), that can differentiate between the alert and drowsy states effectively. Significant features of both modalities (EEG and ECG) are then combined to investigate the improvement in performance using support vector machine (SVM) classifier. The other main contribution of this paper is the study on channel reduction and its impact to the performance of detection. The proposed method demonstrated that combining EEG and ECG has improved the system’s performance in discriminating between alert and drowsy states, instead of using them alone. Our channel reduction analysis revealed that an acceptable level of accuracy (80%) could be achieved by combining just two electrodes (one EEG and one ECG), indicating the feasibility of a system with improved wearability compared with existing systems involving many electrodes. Overall, our results demonstrate that the proposed method can be a viable solution for a practical driver drowsiness system that is both accurate and comfortable to wear.

https://www.mdpi.com/1424-8220/17/9/1991/pdf

A Hybrid Approach to Detect Driver Drowsiness Utilizing Physiological Signals to Improve System Performance and Wearability.

Wearable, multisensor, consumer devices that estimate sleep are now commonplace, but the algorithms used by these devices to score sleep are not open source, and the raw sensor data is rarely accessible for external use. As a result, these devices are limited in their usefulness for clinical and research applications, despite holding much promise. We used a mobile application of our own creation to collect raw acceleration data and heart rate from the Apple Watch worn by participants undergoing polysomnography, as well as during the ambulatory period preceding in lab testing. Using this data, we compared the contributions of multiple features (motion, local standard deviation in heart rate, and "clock proxy") to performance across several classifiers. Best performance was achieved using neural nets, though the differences across classifiers were generally small. For sleep-wake classification, our method scored 90% of epochs correctly, with 59.6% of true wake epochs (specificity) and 93% of true sleep epochs (sensitivity) scored correctly. Accuracy for differentiating wake, NREM sleep, and REM sleep was approximately 72% when all features were used. We generalized our results by testing the models trained on Apple Watch data using data from the Multi-ethnic Study of Atherosclerosis (MESA), and found that we were able to predict sleep with performance comparable to testing on our own dataset. This study demonstrates, for the first time, the ability to analyze raw acceleration and heart rate data from a ubiquitous wearable device with accepted, disclosed mathematical methods to improve accuracy of sleep and sleep stage prediction.

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Sleep stage prediction with raw acceleration and photoplethysmography heart rate data derived from a consumer wearable device.

Sleep is a physiological process involving different biological systems, from molecular to organ level; its integrity is essential for maintaining health and homeostasis in human beings. Although in the past sleep has been considered a state of quiet, experimental and clinical evidences suggest a noteworthy activation of different biological systems during sleep. A key role is played by the autonomic nervous system (ANS), whose modulation regulates cardiovascular functions during sleep onset and different sleep stages. Therefore, an interest on the evaluation of autonomic cardiovascular control in health and disease is growing by means of linear and non-linear heart rate variability (HRV) analyses. The application of classical tools for ANS analysis, such as HRV during physiological sleep, showed that the rapid eye movement (REM) stage is characterized by a likely sympathetic predominance associated with a vagal withdrawal, while the opposite trend is observed during non-REM sleep. More recently, the use of non-linear tools, such as entropy-derived indices, have provided new insight on the cardiac autonomic regulation, revealing for instance changes in the cardiovascular complexity during REM sleep, supporting the hypothesis of a reduced capability of the cardiovascular system to deal with stress challenges. Interestingly, different HRV tools have been applied to characterize autonomic cardiac control in different pathological conditions, from neurological sleep disorders to sleep disordered breathing (SDB). In summary, linear and non-linear analysis of HRV are reliable approaches to assess changes of autonomic cardiac modulation during sleep both in health and diseases. The use of these tools could provide important information of clinical and prognostic relevance.

/pdf/heart-rate-variability-in-normal-and-pathological-sleep-45j5qcimv4.pdf

Heart rate variability in normal and pathological sleep

Summary
Background: Recent experimental data indicate a pathogenic role of complement activation in congestive heart failure (CHF). Aim of this study was to evaluate contact and complement systems activation in patients hospitalized for an acute episode of CHF.

Methods and results: 42 of 80 consecutive patients admitted at our hospital with confirmed diagnosis of acute CHF were enrolled. They underwent blood sampling within 24 hours from admission (T0) and at clinical stability (T1). Patients were stratified for ejection fraction (EF) based on echocardiographic test. We measured plasma levels of C3, C4, sC5b-9 and cleaved high molecular weight kininogen (contact activation marker). At T1, C3 levels significantly increased compared to T0 (97±2 vs 104±3% of total pooled plasma, p<0.01). Classifying patients according to EF, only patients with preserved EF presented a significant increase of C3 from T0 to T1 (99±3 vs 108±4%, p=0.03). When the sample was stratified according to clinical outcome, C3 (98±3 vs 104±4%, p=0.03) and sC5b-9 levels (204±10 vs 230±11 ng/ml, p=0.03) were increased in patients who had positive outcome after hospitalization. CHF patients with preserved EF and positive outcome after hospitalization showed in T1 period higher levels of sC5b-9 compared with T0 (211±14 vs 243±14 ng/ml, p=0.04)

Conclusions: Our results suggest that the complement system reacts differently if CHF occurs with preserved or reduced EF. This finding is interesting if we consider the difference in epidemiology, pathogenesis and possible therapeutic approaches of these two clinical entities. This article is protected by copyright. All rights reserved.

https://onlinelibrary.wiley.com/doi/pdf/10.1111/cei.13011

Complement and contact system activation in acute congestive heart failure patients

Exploring the association between frailty and mortality in a cohort of patients with COVID-19 respiratory insufficiency treated with continuous positive airway pressure. Frailty was measured using a Frailty Index (FI) created by using the baseline assessment data on comorbidities and body mass index and baseline blood test results (including pH, lactate dehydrogenase, renal and liver function, inflammatory indexes and anemia). FI > 0.25 identified frail individuals. Among the 159 included individuals (81% men, median age of 68) frailty was detected in 69% of the patients (median FI score 0.3 ± 0.08). Frailty was associated to an increased mortality (adjusted HR 1.99, 95% CI 1.02–3.88, p = 0.04). Frailty is highly prevalent among patients with COVID-19, predicts poorer outcomes independently of age. A personalization of care balancing the risk and benefit of treatments (especially the invasive ones) in such complex patients is pivotal. 

/pdf/frailty-as-a-predictor-of-mortality-in-covid-19-patients-3tpvpqfz.pdf

Silvia Strada

Papers

Heart rate variability in normal and pathological sleep

Complement and contact system activation in acute congestive heart failure patients

Frailty as a predictor of mortality in COVID-19 patients receiving CPAP for respiratory insufficiency